Distance dependent error mitigation in real-time kinematic (RTK) positioning

ABSTRACT

A method for mitigating atmospheric errors in code and carrier phase measurements based on signals received from a plurality of satellites in a global navigation satellite system is disclosed. A residual tropospheric delay and a plurality of residual ionospheric delays are modeled as states in a Kalman filter. The state update functions of the Kalman filter include at least one baseline distance dependant factor, wherein the baseline distance is the distance between a reference receiver and a mobile receiver. A plurality of ambiguity values are modeled as states in the Kalman filter. The state update function of the Kalman filter for the ambiguity states includes a dynamic noise factor. An estimated position of mobile receiver is updated in accordance with the residual tropospheric delay, the plurality of residual ionospheric delays and/or the plurality of ambiguity values.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/119,451, filed May 12, 2008 , now U.S. Pat. No. 8,035,552 whichclaimed priority to U.S. Provisional Application No. 60/941,273, filedMay 31, 2007, “Distance Dependent Error Mitigation in Real-TimeKinematic (RTK) Positioning,” which is incorporated by reference hereinin its entirety.

This application is related to U.S. patent application Ser. No.12/119,450, filed May 12, 2008, “Partial Search Carrier-Phase IntegerAmbiguity Resolution,” issued as U.S. Pat. No. 7,961,143, whichapplication is incorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to technologies associatedwith positioning systems, such as the Global Positioning System (GPS) orthe European Galileo System, and more particularly to methods ofmitigating atmospheric errors in code and carrier phase measurements.

BACKGROUND

A wide-area positioning system, such as the Global Positioning System(GPS), uses a constellation of satellites to position or navigateobjects on earth. Each satellite in the GPS system currently transmitstwo carrier signals, L1 and L2, with frequencies of 1.5754 GHz and1.2276 GHz, and wavelengths of 0.1903 m and 0.2442 m, respectively. Nextgeneration Global Navigation Satellite Systems (GNSS), such as themodernized GPS and Galileo systems, will offer a third carrier signal:L5. In the GPS system, L5 will have a frequency of 1.1765 GHz, and awavelength of 0.2548 m.

Two types of GPS measurements are usually made by a GPS receiver:pseudorange measurements and carrier phase measurements.

The pseudorange measurement (or code measurement) is a basic GPSobservable that all types of GPS receivers can make. It utilizes the C/Aor P codes modulated onto the carrier signals. With the GPS measurementsavailable, the range or distance between a GPS receiver and each of aplurality of satellites is calculated by multiplying a signal's traveltime (from the satellite to the receiver) by the speed of light. Theseranges are usually referred to as pseudoranges because the GPSmeasurements may include errors due to various error factors, such assatellite clock timing error, ephemeris error, ionospheric andtropospheric refraction effects, receiver tracking noise and multipatherror, etc. To eliminate or reduce these errors, differential operationsare used in many GPS applications. Differential GPS (DGPS) operationstypically involve a base reference GPS receiver, a user GPS receiver,and a communication mechanism between the user and reference receivers.The reference receiver is placed at a known location and is used togenerate corrections associated with some or all of the above errorfactors. Corrections generated at the reference station, or raw datameasured at the reference station, or corrections generated by a thirdparty (e.g., a computer or server) based on information received fromthe reference station (and possibly other reference stations as well)are supplied to the user receiver, which then uses the corrections orraw data to appropriately correct its computed position.

The carrier phase measurement is obtained by integrating a reconstructedcarrier of the signal as it arrives at the receiver. Because of anunknown number of whole cycles in transit between the satellite and thereceiver when the receiver starts tracking the carrier phase of thesignal, there is a whole-cycle ambiguity in the carrier phasemeasurement. This whole-cycle ambiguity must be resolved in order toachieve high accuracy in the carrier phase measurement. Whole-cycleambiguities are also known as “integer ambiguities,” after they havebeen resolved, and as “floating ambiguities” prior to their resolution.Differential operations using carrier phase measurements are oftenreferred to as real-time kinematic (RTK) positioning/navigationoperations.

High precision GPS RTK positioning has been widely used for manysurveying and navigation applications on land, at sea and in the air.The distance from the user receiver to the nearest reference receivermay range from a few kilometers to hundreds of kilometers. As thereceiver separation (i.e., the distance between a reference receiver anda mobile receiver whose position is being determined) increases, theproblem of accounting for distance-dependent biases grows and, as aconsequence, reliable ambiguity resolution becomes an even greaterchallenge. The major challenge is that the residual biases or errorsafter double-differencing can only be neglected for ambiguity resolutionpurposes when the distance between the two receivers is less than about10 km. For longer distances the distance-dependent errors, such asorbital error and ionospheric and tropospheric delays, becomesignificant problems. Determining how long the observation span shouldbe to obtain reliable ambiguity resolution is a challenge for GPS RTKpositioning. The longer the observation span that is required, thelonger the “dead” time during which precise positioning is not possible.The ambiguity resolution process is required at the start of GPSnavigation and/or surveying and whenever to many of the GPS signals areblocked or attenuated such that cycle slips or measurement interruptionsoccur. Quality control of the GPS RTK positioning is critical and isnecessary during all processes: data collection, data processing anddata transmission. Quality control procedures are applied to both thecarrier phase-based GPS RTK positioning and to the pseudo-range-basedDGPS. The quality control and validation criterion for ambiguityresolution represents a significant challenge to precise GPS RTKpositioning.

SUMMARY OF EMBODIMENTS

A method for mitigating distance dependent atmospheric errors in codeand carrier phase measurements includes estimating a residualtropospheric delay, a plurality of residual ionospheric delays and anambiguity value. An estimated position of a mobile receiver is thenupdated in accordance with these estimates.

In one embodiment, a residual tropospheric delay is modeled as a statein a Kalman filter. In one embodiment, a plurality of residualionospheric delays are modeled as states in a Kalman filter. The stateupdate functions of the Kalman filter include at least one baselinedistance dependent factor. The baseline distance dependent factorcorresponds to a distance between a reference receiver and a mobilereceiver.

In one embodiment a plurality of ambiguity values are stored in aplurality of states in the Kalman filter. These states are then updatedin accordance with a state update function that includes at least onedynamic noise factor.

The estimation of the atmospheric error sources limits the distancedependent errors of the GPS RTK systems and allows for longer-rangeapplications with precise position estimates.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a global navigation satellite system.

FIG. 2 is a block diagram of a computer system that can be used to carryout a method for mitigating atmospheric errors in code and carrier phasemeasurements.

FIGS. 3A and 3B are flow diagrams illustrating a method for mitigatingatmospheric errors in code and carrier phase measurements in accordancewith some embodiments.

FIG. 4 is a block diagram illustrating components in a global navigationsatellite system in accordance with some embodiments.

Like reference numerals refer to corresponding parts throughout thedrawings.

DESCRIPTION OF EMBODIMENTS

FIG. 1 illustrates a global navigation satellite system 100, accordingto one embodiment of the present invention. The global navigationsatellite system (GNSS) includes a plurality of satellites 110-1, 110-2,. . . , 110-n, where n is the number of satellites in view of a mobilereceiver 120 and a reference receiver 130, which is typically located ata known, previously established position. The plurality of satellites110-n, or any one or more of them, are sometimes referred to hereafterin this document as satellites(s) 110.

The mobile receiver 120 takes code and carrier phase measurements of theGPS signals 142 and 146 received from the satellites 110. The referencereceiver 130 takes code and carrier phase measurements of the GPSsignals 144 and 148 received from the satellites 110 and generatescorrections 132 to those measurements, based at least in part on thepreviously established location of the reference receiver. Thecorrections 132 are then communicated to the mobile receiver 120. Whilethe description in this document frequently uses the terms “GPS” and“GPS signals” and the like, the present invention is equally applicableto other GNSS systems and the signals from the GNSS satellites in thosesystems.

The baseline distance 150 and the height difference 155 between themobile receiver 120 and the reference receiver 130 are equal to l and ΔHmeters, respectively. The baseline distance 150 represents thehorizontal component of the distance between the mobile receiver 120 andthe reference receiver 130. With respect to the mobile receiver 120, thesatellite elevation 160 for GPS signals 142 and 146 is α_(m)° andθ_(m)°, respectively. With respect to the reference receiver 130, thesatellite elevation 160 for GPS signals 144 and 148 is α_(r)° andθ_(r)°, respectively.

GPS signals 142, 144, 146, 148 are transmitted by the satellites 110through the ionosphere 185 and the troposphere 190 of earth.

The troposphere 190 extends from earth's surface 195 up to about 16 kmin height and is composed of dry gases and water vapor. The GPS signals142, 144, 146, 148 are refracted by the troposphere 190. The magnitudeof the tropospheric delay is dependent upon the satellite elevationangle 160 (from the receiver to the satellite). The tropospheric delayis equal to about 2.3 m in the zenith direction (an elevation angle of90 degrees) and increases to over 25 m for an elevation angle 160 offive degrees. The dry component can be modeled with high accuracy, butthe smaller wet component is much more difficult to model. Thedifferential tropospheric delay of mainly the wet component variestypically from about 0.2 to 0.4 parts per million (ppm) of the baselinedistance 150. The spatial and temporal characteristics of the residualtropospheric delay can be characterized by probabilistic laws orstatistical models. The effects of the troposphere on radio wavepropagation then can be predicted over varying spatial dimensions andtemporal scales according to a given probability density function orstochastically in terms of the spatial and temporal correlations of thefluctuations. In one embodiment, the residual tropospheric delay can beconsidered a first-order Gauss-Markov process.

The ionosphere 185 starts at about 50 km above earth's surface 195 andextends to heights of 1000 km or more. Solar radiation in the ionosphere185 causes atoms to ionize such that free electrons exist in sufficientquantities to significantly affect the propagation of radio waves. Theionosphere 185 advances the carrier phase, which causes the carrierphase measurements to be decreased, but, delays the code modulation,which causes the code measurements to be increased. The magnitude of theionospheric delay is dependent upon the frequency of the signal and uponsolar radiation effects. Therefore, the ionospheric delay is differentfor daytime and nighttime and from one season to another. Diurnally, theionospheric delay usually reaches a first peak at approximately 14:00local time, a second peak at approximately 22:00 local time, and dropsto a minimum just before sunrise. Under extreme conditions, theionospheric delay can reach 15 m in the zenith direction and more than200 m at elevations near the horizon. The ionosphere is typically thelargest error source for differential processing and varies from onepart per million (ppm) of the baseline distance 150 during lowionospheric periods at mid latitudes to greater than 10 ppm at lowgeomagnetic latitudes during midday. The GPS satellites broadcast inreal time correction data (e.g., coefficients of the Klobuchar model)that enables single-frequency receivers to remove, on average, aboutfifty percent of the ionospheric refraction effects.

FIG. 2 illustrates a computer system 200 that can be used to carry out amethod for mitigating atmospheric errors, according to one embodiment ofthe present invention. The computer system 200 is coupled to a mobilereceiver 120 which supplies to the computer system 200 GPS code andcarrier phase measurements based on signals from the satellites.

In some embodiments, the mobile receiver 120 and the computer system 200are integrated into a single device within a single housing, such as aportable, handheld, or even wearable position tracking device, or avehicle-mounted or otherwise mobile positioning and/or navigationsystem. In other embodiments, the mobile receiver 120 and the computersystem 200 are not integrated into a single device.

As shown in FIG. 2, the computer system 200 includes a centralprocessing unit (CPU) 240, memory 250, an input port 242 and an outputport 244, and (optionally) a user interface 246, coupled to each otherby one or more communication buses 248. Memory 250 may includehigh-speed random access memory and may include nonvolatile massstorage, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. Memory 250 preferably stores an operating system 252, adatabase 256, and GNSS application procedures 254. The GNSS applicationprocedures may include procedures 255 for implementing the method formitigating atmospheric errors, according to some embodiments of thepresent invention, as described in more detail below. The operatingsystem 252 and application programs and procedures 254 and 255 stored inmemory 250 are for execution by the CPU 240 of the computer system 200.Memory 250 preferably also stores data structures used during executionof the GNSS application procedures 254 and 255, including GPS codeand/or carrier phase measurements 257, as well as other data structuresdiscussed in this document.

The input port 242 is for receiving data from the mobile receiver 120,and output port 244 is used for outputting data and/or calculationresults. Data and calculation results may also be shown on a displaydevice of the user interface 246.

FIGS. 3A and 3B illustrate a navigation method 300 that includesoperations for mitigating atmospheric errors in the code and carrierphase measurements based on signals received from the satellites. Whilean explanation of Kalman filters is outside the scope of this document,the computer system 200 typically includes a Kalman filter for updatingthe position and other aspects of the state of the user GPS receiver120, also called the Kalman filter state. The Kalman filter stateactually includes many states, each of which represents an aspect of theGPS receiver's position (e.g., X, Y and Z, or latitude, longitude andzenith components of position), or motion (e.g., velocity and/oracceleration), or the state of the computational process that is beingused in the Kalman filter.

The Kalman filter is typically a procedure, or set of procedures,executed by a processor. The Kalman filter is executed repeatedly (e.g.,once per second), each time using new code measurements (also calledpseudorange measurements) and carrier phase measurements, to update theKalman filter state. While the equations used by Kalman filters arecomplex, Kalman filters are widely used in the field of navigation, andtherefore only those aspects of the Kalman filters that are relevant tothe present invention need to be discussed in any detail. It should beemphasized that while Kalman filters are widely used in GPS receiversand other navigation systems, many aspects of those Kalman filters willvary from one implementation to another. For instance, the Kalmanfilters used in some GPS receivers may include states that are notincluded in other Kalman filters, or may use somewhat differentequations than those used in other Kalman filters.

An aspect of Kalman filters that is relevant to the present discussionis the inclusion of values in the Kalman filter state to representtropospheric delay and ionospheric delay of the signals received fromthe satellites in view, and the status of those values. In addition, theKalman filter state may include ambiguity values for the carrier phasemeasurements from a plurality of the satellites.

As stated above in regard to FIG. 1, signals are received from thesatellites 110 and corrections 132 are received from the referencereceiver 310. Operation 310 includes obtaining code and carrier phasemeasurements. Double differenced code and carrier phase measurements areformed 320 to cancel many of the systematic errors existing in the GPSmeasurements. The double differenced code and carrier phase observablesin units of meters can be formed as:

$\begin{matrix}{{{\nabla\Delta}\; P_{i}} = {{{\nabla\Delta}\;\rho} + {{\nabla\Delta}\; T} + \frac{{\nabla\Delta}\; I}{f_{i}^{2}} + {{\nabla\Delta}\; O} + ɛ_{{\nabla\Delta}\; P_{i}}}} & (1) \\{{\lambda_{i}{\nabla{\Delta\phi}_{i}}} = {{\nabla{\Delta\rho}} + {{\nabla\Delta}\; T} - \frac{{\nabla\Delta}\; I}{f_{i}^{2}} + {{\nabla\Delta}\; O} + {{\lambda_{i} \cdot {\nabla\Delta}}\; N_{i}} + ɛ_{\nabla{\Delta\phi}_{i}}}} & (2)\end{matrix}$where: the subscript i denotes the frequency, i.e., L1, L2 or L5; P_(i)and φ_(i) are the code and carrier phase observables, respectively; ∇Δis the double difference operator; ρ is the geometric distance from thesatellite to the receiver; ∇ΔT is the residual differential troposphericbias, which may be represented as a function of the residual zenithtropospheric delay together with a mapping function which describes thedependence on the elevation angle; Δ∇I is the double differentialionospheric bias; ∇ΔO is the double differential orbital delaycorrection that may be obtained from a network RTK system or a wide areaaugmentation system (WAAS), such as Navcom Technology Inc.'s StarFire™Network; λ_(i) and f_(i) are the wavelength and frequency of the i^(th)carrier frequency, respectively; ∇ΔN_(i) is the double differenceinteger ambiguity for the i^(th) carrier frequency; and the termsε_(∇ΔP) _(i) and ε_(∇Δφ) _(i) represent the code and phase errors,respectively, including random noises of receivers and any unmodeledsystematic errors, such as multipath, residual orbit errors, etc.

Linearization of the double differenced carrier phase observations canbe represented by the following set of equations:V=HX−Z  (3)where: V is the post-fit residual vector at epoch k; Z is the prefitresiduals, which are based on the double difference measurements for thecurrent epoch; H is the design matrix; and X is the estimated statevector including three position components, residual ionospheric andtropospheric biases, and dual or triple frequency ambiguities. Thevalues for the estimated state vector X are stored in Kalman filterstates.

In one embodiment, the Kalman filter includes a plurality of states,including but not limited to: three position states (472, FIG. 4), eachcorresponding to a different direction or dimension; a residualtropospheric delay state (474, FIG. 4); and N−1 residual ionosphericdelay states (476, FIG. 4). The Kalman filter state may optionallyinclude three velocity states each corresponding to a differentdirection or dimension, and may optionally include three accelerationstates each corresponding to a different direction or dimension (472,FIG. 4). In some embodiments, the Kalman filter state includes N−1 L1double differenced ambiguity states (478, FIG. 4), and N−1 L2 doubledifferenced ambiguity states (479, FIG. 4), where N is the number ofsatellites from which measurements are obtained.

In one embodiment, Kalman filter projections and state updates areobtained. If the Kalman filter estimates after k−1 epochs are assumed tobe {circumflex over (x)}_(k−1) ⁺ with variance P_(k−1) ⁺, the predictedstate vector at the epoch k can be obtained from State Equations (4) and(5):{circumflex over (X)} _(k) ⁻=Φ_(k−1,k) X _(k−1) ⁺  (4)P _(k) ⁻=Φ_(k−1,k) P _(k−1) ⁺Φ_(k−1,k) ^(T) +W _(k)  (5)where: {circumflex over (X)}_(k) ⁻ is the predicted Kalman filter statevector at epoch k, predicted based on the Kalman filter state in epochk−1; Φ_(k,k−1) is the transition matrix that relates X_(k−1) to X_(k);and W_(k) is the dynamic matrix. W_(k) includes the residualtropospheric delay, residual ionospheric delay values and an ambiguityvalue.

The updated state and variance matrix using the measurement vectors atepoch k are given by the following equations:{circumflex over (X)} _(k) ⁺ ={circumflex over (X)} _(k) ⁻ +KZ  (6)P _(k) ⁺=(I−KH)P _(k) ⁻  (7)K=P _(k) ⁻ H(HP _(k) ⁻ +H+R)⁻¹  (8)where: K is the Gain matrix; R is variance covariance for observables;and I is identity matrix.

As shown in FIG. 3A, the method 300 includes estimating the atmosphericerrors in the code and carrier phase measurements 330, which may includetwo or more of the operations described next.

The residual tropospheric delay is estimated 340. In one embodiment,this includes representing the tropospheric delay as a residualtropospheric zenith delay (RTZD) and a mapping function 342 to obtainthe delay at any given satellite elevation angle 160. All the deviationsof the atmospheric conditions from standard conditions are subsumedwithin the RTZD. After the tropospheric delay model is applied, theresidual double differential tropospheric delay can be approximated by:∇ΔT=RTZD/[MF(ε^(p))−MF(ε^(q))]  (9)where: ε^(p) and ε^(q) are the average satellite elevation angles of themobile receiver 120 and the reference receiver 130 for satellites p andq, respectively; satellite q is the highest satellite 110; and satellitep is any other satellite 110 from which the receiver is receivingmeasurable signals. In one embodiment, a single RTZD estimate is usedfor all visible satellites. The RTZD value is a component of the Kalmanfilter state (i.e., the tropospheric delay component), and is updatedeach epoch by the Kalman state update function. Therefore, no matterwhat the satellite elevation 160 is, the ∇ΔT in Equations (1) and (2)will be scaled by the map function factor of the mobile receiver 120 andthe reference receiver 130 location using Equation (9).

In one embodiment, estimating the residual tropospheric delay 340includes modeling the residual tropospheric delay (e.g., the RTZD) as astate in the Kalman filter and using a state update function thatincludes at least one baseline distance 150 dependent factor (i.e.,corresponding to a distance between a reference receiver and the mobilereceiver whose location is being determined). In some embodiments,estimating the residual tropospheric delay 340 includes using a stateupdate function with at least one factor based on the baseline distance150 and height difference 155 between the reference and mobile receivers344. In some of these embodiments, the transition matrix φ_(k,k−1) anddynamic model Q_(k) are given by:

$\begin{matrix}{\phi_{k,{k - 1}} = {\mathbb{e}}^{- {\beta_{trop}{({t_{k} - t_{k - 1}})}}}} & (10) \\{Q_{k} = {\frac{\sigma_{trop}^{2}}{2\beta_{trop}}\left( {1 - {\mathbb{e}}^{{- 2}{\beta_{trop}{({t_{k} - t_{k - 1}})}}}} \right)}} & (11) \\{\sigma_{trop}^{2} = {{\sigma_{hor}^{2} \times l^{2}} + {\sigma_{v}^{2} \times \Delta\; H^{2}}}} & (12)\end{matrix}$where: 1β_(trop) is the correlation time of the troposphere wetcomponent, which is typically between 600 and 1800 seconds; σ_(trop) ²is the troposphere wet variance component and is a function of thebaseline distance l and height difference ΔH; σ_(hor) ² is the variancefor horizontal wet component, typically between 0.1 ppm and 0.5 ppm ofthe baseline distance; l and σ_(v) ² is the variance for the verticalwet component, typically between 1 ppm and 10 ppm of the baselinedistance l. Q_(k) and φ_(k,k−1) are the residual tropospheric delayportions of W_(k) in Equation (5) and Φ_(k,k−1) in Equation (4),respectively. In some embodiments, σ_(hor) ² is set to a fixed value,such as 0.1 ppm, and 1/β_(trop) is set to a fixed value, such as 600seconds. In some other embodiments, the values of σ_(hor) ² and1/β_(trop) are computed based on information available to the mobilereceiver, such as the baseline distance between the mobile receiver andthe reference receiver. In some embodiments, the values of σ_(hor) ² and1/β_(trop) are obtained from a look-up-table, using the baselinedistance between the mobile receiver and the reference receiver (or avalue related to the baseline distance) as an index into the look-uptable.

At least one residual ionospheric delay is estimated 350. In oneembodiment, after the code and carrier phase measurements are adjustedby the broadcast ionospheric model and differenced with the corrections132 from the reference receiver 130, the remaining ionospheric delay isestimated in a Kalman filter as an element of the state vector. In oneembodiment, estimating the residual ionospheric delay 350 includesmodeling the residual ionospheric delay as a state in the Kalman filterand using a state update function that includes at least one baselinedistance 150 dependent factor. In another embodiment, a state updatefunction with at least one factor based on the local time and ionosphereactivity is used 354. In this embodiment, the transition matrixφ_(k−1,k) and dynamic model Q_(k) of the state update function are givenby:

$\begin{matrix}{\phi_{{k - 1},k} = {\mathbb{e}}^{- {\beta_{ion}{({t_{k} - t_{k - 1}})}}}} & (13) \\{Q_{k} = {\frac{\sigma_{sion}^{2}}{2\beta_{ion}}\left( {1 - {\mathbb{e}}^{{- 2}{\beta_{ion}{({t_{k} - t_{k - 1}})}}}} \right)}} & (14) \\{\sigma_{sion} = \frac{\sigma_{vion} \times l}{\sqrt{1 - \left( {\frac{R}{R + H}{\cos(E)}} \right)^{2}}}} & (15)\end{matrix}$where: 1/β_(ion) is the correlation time of the differential ionospherebias, typically between 30 and 300 seconds; σ_(sion) and σ_(vion)represent the variance of the differential slant and vertical ionospherebiases, and σ_(vion) is a function of the local time and ionosphereactivity; l is the baseline distance 150; E is satellite elevation 160;H is the height of the ionospheric layer 185, which may be assumed to be350 km, for example; and R is 6371 km, the mean radius of the earth.σ_(vion) typically varies between 0.5 ppm and 2 ppm of the baselinedistance 150. Q_(k) and φ_(k,k−1) are the residual ionospheric delayportions of W_(k) in Equation (5) and Φ_(k,k−1) in Equation (4),respectively. In some embodiments, σ_(vion) is set to a fixed value,such as 1 ppm, and 1/β_(ion) is set to a fixed value, such as 30seconds. In some embodiments, the values of σ_(vion) and 1/β_(ion) arecomputed based on information available to the mobile receiver, such asthe local time computed from the preliminary GPS solution using the GMTor GPS time and the computed longitude of the receiver. In someembodiments, the values of σ_(hor) ² and 1/β_(trop) are obtained from alook-up-table.

Unlike residual tropospheric bias, the residual ionosphere delay isestimated for every satellite other than the reference satellite 352.Therefore, there will be N−1 residual ionospheric bias estimations andN−1 Kalman filter state values representing the N−1 residual ionosphericbias estimations.

In some embodiments, the method further includes accessing a pluralityof states in the Kalman filter, corresponding to a plurality ofambiguity values 360. These states are then updated in accordance with astate update function that includes at least one dynamic noise factor.The transition matrix φ_(k−1,k) and dynamic model Q_(k) of the stateupdate function are given byφ_(k−1,k)=1  (16)Q _(k)=δ_(amb) ²(t _(k) −t _(k−1))  (17)where: δ_(amb) is a small dynamic noise value such as 0.001 cycle. Q_(k)and φ_(k,k−1) are the ambiguity value portions of W_(k) in Equation (5)and Φ_(k,k−1) in Equation (4), respectively.

In some embodiments, the navigation method 300 includes updating anestimated position of the mobile receiver 120 (370). Typically, theestimated position is updated in accordance with the double differencedcode and carrier phase measurements 378, as well as other informationavailable to the mobile receiver (or to the computer system that isdetermining the location of the mobile receiver). In some embodiments,the estimated position is updated in accordance with the estimatedresidual tropospheric delay 372. In some embodiments, the estimatedposition is updated in accordance with the estimated residualionospheric delay 374. In some of these embodiments, a distinct residualionospheric delay state in the Kalman filter is updated 375 for each ofa plurality of satellites 110 (e.g., for all of the satellites in viewother than the one most directly overhead). In some embodiments, theestimated position is updated in accordance with the ambiguity statevalues in the Kalman filter state 376.

FIG. 4 illustrates an embodiment of the computer system 200. Thecomputer system 200 includes a signal processor 420, at least oneprocessor 430 and a memory 250. Memory 250, which may include high-speedrandom access memory and may also include non-volatile memory, such asone or more magnetic disk storage devices, EEPROM and/or Flash EEPROM,includes an operating system 252, code and carrier phase measurements257, a Kalman filter update program 460, a Kalman filter state 470, andat least one atmospheric error estimation program module 255, executedby processor 430. Stored in the Kalman filter state 470 is a pluralityof state values: a position 472, a residual tropospheric delay value474, a plurality (e.g., N−1) of residual ionospheric delay values 476, aplurality (e.g., N−1) L1 integer ambiguity values 478, and a plurality(e.g., N−1) L2 integer ambiguity values 479, each of which has beendiscussed above. The at least one atmospheric error estimation programmodule 255 includes at least one residual ionospheric delay estimationprogram 552, at least one residual tropospheric delay estimation program554, and at least one integer ambiguity value estimation program 556.

In some embodiments there may be more than one processor 430. In otherembodiments, the computer system 200 may include an application specificintegrated circuit (ASIC) that performs some or all of the functionalityof the atmospheric error estimation program module 255.

In some embodiments, the computer system 200 is coupled to a receiver410, such as the mobile receiver 120 (FIG. 1). In other embodiments, thecomputer system 200 and the receiver 410 are integrated into a singledevice.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method for mitigating atmospheric errors in code and carrier phasemeasurements based on signals received from a plurality of satellites ina global navigation satellite system, the method comprising: estimatingat least one residual ionospheric delay, the at least one residualionospheric delay modeled as at least one state in the Kalman filter,and wherein a state update function of the Kalman filter for the atleast one residual ionospheric delay includes at least one baselinedistance dependent factor, wherein the at least one baseline distancedependent factor corresponds to a distance between a reference receiverand a mobile receiver; and updating an estimated position of the mobilereceiver in accordance with the at least one estimated residualionospheric delays and the code and carrier phase measurements, whereinthe estimated position of the mobile receiver is modeled as a state inthe Kalman filter.
 2. The method of claim 1, including: obtaining codeand carrier phase measurements based on signals received from theplurality of satellites at the reference receiver and mobile receiver;computing double difference values from the obtained measurements toform double difference code and carrier phase measurements; and updatingthe estimated position of the mobile receiver in accordance with the atleast one estimated residual ionospheric delays and the doubledifference code and carrier phase measurements.
 3. The method of claim1, wherein the updating includes updating a distinct residualionospheric delay state in the Kalman filter for each of a plurality ofsatellites.
 4. The method of claim 1, wherein the state update functionof the Kalman filter for the at least one residual ionospheric delayincludes the least one baseline distance dependent factor and asatellite elevation dependent factor.
 5. A method for mitigatingatmospheric errors in code and carrier phase measurements based onsignals received from a plurality of satellites in a global navigationsatellite system, the method comprising: estimating N−1 residualionospheric delays, the N−1 residual ionospheric delays modeled as N−1states in the Kalman filter, and wherein a state update function of theKalman filter for the N−1 residual ionospheric delays includes at leastone baseline distance dependent factor for each of the N−1 states,wherein N comprises a number of satellites from which signals arereceived and for which code and carrier phase measurements are made, andwherein the at least one baseline distance dependent factor correspondsto a distance between a reference receiver and a mobile receiver; andupdating an estimated position of the mobile receiver in accordance withthe N−1 estimated residual ionospheric delays and the code and carrierphase measurements, wherein the estimated position of the mobilereceiver is modeled as a state in the Kalman filter.
 6. The method ofclaim 5, including: obtaining code and carrier phase measurements basedon signals received from the plurality of satellites at the referencereceiver and mobile receiver; computing double difference values fromthe obtained measurements to form double difference code and carrierphase measurements; and updating the estimated position of the mobilereceiver in accordance with the estimated N−1 residual ionosphericdelays and the double difference code and carrier phase measurements. 7.The method of claim 5, wherein the updating includes updating a distinctresidual ionospheric delay state in the Kalman filter for each of aplurality of satellites.
 8. The method of claim 5, wherein the stateupdate function of the Kalman filter for the N−1 residual ionosphericdelays includes the least one baseline distance dependent factor and asatellite elevation dependent factor.
 9. A positioning or navigationsystem, comprising: a mobile receiver configured to receive satellitesignals from a plurality of satellites in a global navigation system; acomputer system coupled to the receiver, the computer system including aprocessor and a memory coupled to the processor, the memory storing oneor more programs for mitigating atmospheric errors in code and carrierphase measurements based on the signals received from the satellites,the one or more programs including: instructions for estimating N−1residual ionospheric delays, the N−1 residual ionospheric delays modeledas a state in a Kalman filter, and wherein a state update function ofthe Kalman filter includes at least one baseline distance dependentfactor, wherein the at least one baseline distance dependent factorcorresponds to a distance between a reference receiver and the mobilereceiver; and instructions for updating an estimated position of themobile receiver in accordance with the estimated N−1 residualionospheric delays and the code and carrier phase measurements, whereinthe estimated position of the mobile receiver is modeled as a state inthe Kalman filter.
 10. The system of claim 9, wherein the one or moreprograms include: instruction for obtaining code and carrier phasemeasurements based on signals received from the-plurality of satellitesat the reference receiver and mobile receiver; instruction for computingdouble difference values from the obtained measurements to form doubledifference code and carrier phase measurements; and instruction forupdating the estimated position of the mobile receiver in accordancewith the estimated N−1 residual ionospheric delays and the doubledifference code and carrier phase measurements.
 11. The system of claim9, wherein the updating includes updating a distinct residualionospheric delay state in the Kalman filter for each of a plurality ofsatellites.
 12. The system of claim 9, wherein the state update functionof the Kalman filter for the N−1 residual ionospheric delays includes atleast one factor based on local time and ionopshere activity.
 13. Thesystem of claim 9, wherein the state update function of the Kalmanfilter for N−1 residual ionospheric delays includes the least onebaseline distance dependent factor and a satellite elevation dependentfactor.
 14. A positioning or navigation device, comprising: a mobilereceiver configured to receive satellite signals from a plurality ofsatellites in a global navigation system; memory; one or moreprocessors; one or more programs stored in the memory for execution bythe one or more processors, the one or more programs for mitigatingatmospheric errors in code and carrier phase measurements based on thesignals received from the satellites, the one or more programsincluding: instructions for estimating N−1 residual ionospheric delays,the N−1 residual ionospheric delays modeled as a state in a Kalmanfilter, and wherein a state update function of the Kalman filterincludes at least one baseline distance dependent factor, wherein the atleast one baseline distance dependent factor corresponds to a distancebetween a reference receiver and the mobile receiver; and instructionsfor updating an estimated position of the mobile receiver in accordancewith the estimated N−1 residual ionospheric delays and the code andcarrier phase measurements, wherein the estimated position of the mobilereceiver is modeled as a state in the Kalman filter.
 15. The device ofclaim 14, wherein the one or more programs include: instruction forobtaining code and carrier phase measurements based on signals receivedfrom the-plurality of satellites at the reference receiver and mobilereceiver; instruction for computing double difference values from theobtained measurements to form double difference code and carrier phasemeasurements; and instruction for updating the estimated position of themobile receiver in accordance with the estimated N−1 residualionospheric delays and the double difference code and carrier phasemeasurements.
 16. The device of claim 14, wherein the updating includesupdating a distinct residual ionospheric delay state in the Kalmanfilter for each of a plurality of satellites.
 17. The device of claim14, wherein the state update function of the Kalman filter for the N−1residual ionospheric delays includes at least one factor based on localtime and ionosphere activity.
 18. The device of claim 14, wherein thestate update function of the Kalman filter for the N−1 residualionospheric delays includes the least one baseline distance dependentfactor and a satellite elevation dependent factor.